Abstract: Embodiments include a multi-tenant cloud system that receives a request for an authenticate action for a user. Embodiments create an authenticate target action and register a cache listener for a cache that includes a filter to listen for a target action response that is responsive to the authenticate target action, the filter listing a plurality of bridges assigned to an on-premise active directory. Embodiments randomly select one of the plurality of bridges and sends the authenticate target action to the active directory via the selected bridge. Embodiments wait for a cache callback and, at the cache callback, receive a target action response that includes a result of the authenticate action.

Abstract: A defect level for a software application may be predicted by training a model using aspects of development processes from previous software applications as training data. Aspects of previous software development processes may be aggregated to form signal vectors for each deployed application. Defect scores calculated from actual defects in the deployed software applications may be paired with the corresponding development signal vectors. The signal vectors and calculated defect scores may act as training data and labels for a predictive model that uses lasso regression to generate a predicted defect score during the development process. A signal vector for a current development process may be updated in real time as the software is developed to update a predicted defect score and provide a subset of aspects in the signal vector that contribute most to the score such that actions may be taken to improve the score.

Abstract: In a resource-pooling system, predictions can be made as to when and how resources may be needed by particular processes in the system. Requests can be made preemptively to client systems to pre-allocate resources such that resources are ready to use when needed. Client systems can submit constraints on how particular resources may be used by the system. In order to efficiently evaluate these constraints, the system may be organized into a hierarchy of groups, subsystems, and processes, and the constraints may be formulated to match this hierarchy. When resources need to be allocated, constraints may be evaluated using an algorithm that traverses levels of the hierarchy to quickly identify pre-allocations that are available for a particular process based on its location in the system hierarchy.

Abstract: In accordance with an embodiment, described herein is a system and method for providing key performance indicator (KPI) customization in an analytic applications environment, which enables data analytics within the context of an organization's enterprise software application or data environment, or a software-as-a-service or other type of cloud or computing environment. The system supports customization derived from multiple layers which, in aggregate, can yield a customized performance metric or KPI object. In accordance with an embodiment, the system enables creation of a customized KPI, by layering variations of the KPI information on an original (e.g., out-of-the-box or factory) KPI object which are merged at runtime to create the final customized KPI. Each delta-KPI can itself also support multiple, e.g., site/user levels/layers.

Abstract: Detecting a counterfeit status of a target device by: selecting a set of frequencies that best reflect load dynamics or other information content of a reference device while undergoing a power test sequence; obtaining target electromagnetic interference (EMI) signals emitted by the target device while undergoing the same power test sequence; creating a sequence of target kiviat plots from the amplitude of the target EMI signals at each of the set of frequencies at observations over the power test sequence to form a target kiviat tube EMI fingerprint; comparing the target kiviat tube EMI fingerprint to a reference kiviat tube EMI fingerprint for the reference device undergoing the power test sequence to determine whether the target device and the reference device are of the same type; and generating a signal to indicate a counterfeit status based at least in part on the results of the comparison.

Abstract: Systems and methods described herein support tag based resource limits or quotas in a cloud infrastructure environment. Cloud administrators do not generally have the ability to restrict resource usage in existing clouds. Granting a user permission to create resources allows them to create any number of resources up to a predefined account limit. Tags are associated with resources for allowing administrators to restrict a user's resource usage to the appropriate level by allowing fine-tuned cost control. A usage of resources in a tenancy associated with a resource tag corresponding to a request characteristic of a request to provision a resource is determined and compared against a plurality of tag-based quotas, wherein the request to provision the resource is dropped based on the determined usage exceeding one of the plurality of tag-based quotas.

Abstract: In accordance with an embodiment, described herein is a system and method for providing a mobile device application and rendering engine, for use with a data analytics environment in generating interactive visualizations of large amounts of data, with high frame rates. The mobile device can receive, from a data analytics server, a data payload describing data points associated with data analytics visualizations, and employs shader objects or routines (shaders) in combination with a graphics processing unit (GPU), to render data visualizations with high resolution, including support for features such as drill-downs. User interaction with a visualization can be interpreted by the rendering engine as transformations or deltas of an original presentation, and communicated to a shader to cause the GPU to render an updated visualization. A hierarchical data model enables data visualizations to be layered and brought forward or backward within the overall presentation in a multi-dimensional exploratory manner.

Abstract: Embodiments match sensor data output by a sensor to a trained pattern. Embodiments form a plurality of windows of an identified pattern from the sensor data, each of the plurality of windows having a substantially equal window length to a length of the trained pattern. For each of the windows, embodiments generate a corresponding first Symbolic Aggregate approximation (“SAX”) word, determine a Hamming distance between the first SAX word and a second SAX word corresponding to the trained pattern, and determine a final distance score based on coefficients between the first SAX word and the second SAX word. For each of the windows, embodiments determine a number of positions in the first SAX word that do not contribute to the final distance score, update the Hamming distance after eliminating the number of positions and determine an average distance based on the final distance score and the updated Hamming distance.

Abstract: A method for conducting a velocity check for outbound subscribers roaming to neighboring countries includes receiving, by a network gateway associated with a home country from an MSC associated with a neighboring country, an ingress signaling message related to a mobile device roaming in the neighboring country, sending, to an HLR in the home country, an interrogation request message containing a mobile subscriber identifier, and receiving an interrogation response message containing age of location information and cell identifier information corresponding to the mobile subscriber identifier.

Abstract: Techniques are described for fast approximate conditional sampling by randomly sampling a dataset and then performing a nearest neighbor search on the pre-sampled dataset to reduce the data over which the nearest neighbor search must be performed and, according to an embodiment, to effectively reduce the number of nearest neighbors that are to be found within the random sample. Furthermore, KD-Tree-based stratified sampling is used to generate a representative sample of a dataset. KD-Tree-based stratified sampling may be used to identify the random sample for fast approximate conditional sampling, which reduces variance in the resulting data sample. As such, using KD-Tree-based stratified sampling to generate the random sample for fast approximate conditional sampling ensures that any nearest neighbor selected, for a target data instance, from the random sample is likely to be among the nearest neighbors of the target data instance within the unsampled dataset.

Abstract: Techniques for loop prevention while allowing multipath in a virtual Layer 2 (L2) network are described. In an example, a network interface card (NIC) supports the virtual L2 network. The NIC is configured to receive, via a first port of the NIC, an L2 frame that includes a source media access control (MAC) address and a destination MAC address. Based on a loop prevention rule, the NIC transmits the L2 frame via its ports except the first port. In an additional example, the NIC is further configured to send an L2 frame to a host via the first port of the NIC. The L2 frame can be a bridge protocol data unit (BPDU). Upon receiving a BPDU from the host via the first port, the NIC determines that the BPDU is looped back and disables the first port.

Abstract: A compiler is capable of compiling instructions that do or do not supply specialization information for a generic type. The generic type is compiled into an unspecialized type. If specialization information was supplied, the unspecialized type is adorned with information indicating type restrictions for application programming interface (API) points associated with the unspecialized type, which becomes a specialized type. A runtime environment is capable of executing calls to a same API point that do or do not indicate a specialized type, and is capable of executing calls to a same API point of objects of an unspecialized type or of objects of a specialized type. When the call to an API point indicates a specialized type, and the specialized type matches that of the object (if the API point belongs to an object), then a runtime environment may perform optimized accesses based on type restrictions derived from the specialized type.

Abstract: A first SBC establishes a first media session between a transferor and a transferee. A second SBC establishes a second media session between the transferor and a transfer target. The first SBC receives a REFER message initiated by the transferor and determines that a dialog ID in the REFER message does not correspond to a media session currently being handled by the first SBC. The first SBC sends a SIP INVITE message to a plurality of SBCs, including the second SBC, that are in a load sharing group with the first SBC. The SIP INVITE message includes the dialog ID associated with the second media session, which triggers the second SBC to re-use the second media session to establish a media session between the transferee and the transfer target.

Abstract: A bill verification system for verifying bill records associated with an entity account is disclosed. The system receives a request to verify a bill record associated with an entity account. The system identifies a bill level model to be used for verifying the bill record and detects based on the bill level model, bill level anomaly information for the bill record. The system additionally identifies a bill line level model to be used for verifying one or more bill lines in the bill record and detects based on the bill line level model, bill line level anomaly information for the bill record. The system then aggregates the bill level anomaly information and the bill line level anomaly information to generate a bill verification report for the bill record. The system provides the bill verification report as a response to the request received to verify the bill record.

Abstract: A data catalog system is described that includes capabilities for automatically identifying and classifying sensitive information stored in data objects associated with various data sources. The data catalog system identifies a data object associated with a data asset stored in a data catalog metadata repository and computes a sensitivity score for the data object based on a set of one or more sensitive data identification techniques. The system determines a set of enrichment labels for the data object based on the sensitivity score computed for the data object. The enrichment labels are used to further qualify, enrich, or classify the data objects identified as containing sensitive information. For instance, the enrichment labels may identify a set of custom properties to be assigned to a data object, identify glossary terms to be applied to the data object or the enrichment labels may identify tags to be assigned to the data object.

Abstract: A Representational State Transfer (REST)-Simple Message Transfer Protocol (SMTP) protocol gateway (RSPG) is disclosed that includes capabilities for accessing and processing REST Application Programming Interfaces (APIs) using SMTP. The RSPG receives a first email message, extracts information from the received first email message and constructs a REST API call based on the extracted information. The RSPG invokes the REST API call against a REST endpoint and receives a response generated from execution of the REST API call. The RSPG generates a second email message based on the response and causes the second email message to be communicated to an intended recipient of the response of the REST API call.

Abstract: Embodiments implement a secure connector framework at a cloud infrastructure. Embodiments receive one or more notebook profiles from an on-premises system corresponding to a first cloud customer, the on-premises system comprising at least one of one or more datasets, one or more models, or one or more libraries, the notebook profiles comprising permission sets that specify a level of access to the datasets, the models and the libraries, the notebook profiles corresponding to an on-premises machine learning (“ML”) notebook. Embodiments transform the received notebook profiles into a cloud policy set for sharing the datasets, the models and the libraries. Embodiments then transmit and receive corresponding data from the first cloud customer to a second cloud customer, the transmitted and received data based on the cloud policy set.

Abstract: A method may include obtaining, for an application, application dependency specifications, identifying vulnerable components using the application dependency specifications and a list of known vulnerable components, selecting, for a vulnerable component, candidate dependency specifications each specifying a version ID for a component, selecting, for a candidate dependency specification, an upgraded version ID for a component, verifying, using an application dependency graph generated from the application dependency specifications, that upgrading the candidate dependency specification to the upgraded version ID removes a dependency on the vulnerable component, and recommending, for the application, an upgrade solution including upgrading the candidate dependency specification to the upgraded version ID.

Abstract: Techniques for selecting and training candidate time-series models to forecast computational workloads are disclosed. A system creates a candidate set of time-series models for forecasting computing workloads by filtering the sets of parameter values to a number that meets system performance specifications. The system selects different sets of parameter values for different candidate models based on analyzing correlogram data. The system identifies in the correlogram data a set of one or more correlation values that (a) meet or exceed a threshold value, and (b) meet a distance criteria from the threshold value. The system trains the candidate set of time-series models with a training data set. The system selects the best-performing time-series model to generate forecasts for a particular computing resource in a computing system.

Abstract: A method of customizing deployment and operation of services in container environments may include receiving, at an API registry, a property for a service that is or will be encapsulated in a container that is or will be deployed in a container environment. The method may also include determining whether the property for the service affects the deployment of the service to the container environment, and in response to a determination that the property affects the deployment of the service, deploying the service based at least in part on the property. The method may additionally include determining whether the property for the service affects the generation of a client library that calls the service in the container environment, and in response to a determination that the property affects the generation of the client library, generating the client library based at least in part on the property.